{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Implementing a Neural Network\n",
    "In this exercise we will develop a neural network with fully-connected layers to perform classification, and test it out on the CIFAR-10 dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# A bit of setup\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from cs231n.classifiers.neural_net import TwoLayerNet\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\" returns relative error \"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "We will use the class `TwoLayerNet` in the file `cs231n/classifiers/neural_net.py` to represent instances of our network. The network parameters are stored in the instance variable `self.params` where keys are string parameter names and values are numpy arrays. Below, we initialize toy data and a toy model that we will use to develop your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# Create a small net and some toy data to check your implementations.\n",
    "# Note that we set the random seed for repeatable experiments.\n",
    "\n",
    "input_size = 4\n",
    "hidden_size = 10\n",
    "num_classes = 3\n",
    "num_inputs = 5\n",
    "\n",
    "def init_toy_model():\n",
    "    np.random.seed(0)\n",
    "    return TwoLayerNet(input_size, hidden_size, num_classes, std=1e-1)\n",
    "\n",
    "def init_toy_data():\n",
    "    np.random.seed(1)\n",
    "    X = 10 * np.random.randn(num_inputs, input_size)\n",
    "    y = np.array([0, 1, 2, 2, 1])\n",
    "    return X, y\n",
    "\n",
    "net = init_toy_model()\n",
    "X, y = init_toy_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forward pass: compute scores\n",
    "Open the file `cs231n/classifiers/neural_net.py` and look at the method `TwoLayerNet.loss`. This function is very similar to the loss functions you have written for the SVM and Softmax exercises: It takes the data and weights and computes the class scores, the loss, and the gradients on the parameters. \n",
    "\n",
    "Implement the first part of the forward pass which uses the weights and biases to compute the scores for all inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your scores:\n",
      "[[-0.81233741 -1.27654624 -0.70335995]\n",
      " [-0.17129677 -1.18803311 -0.47310444]\n",
      " [-0.51590475 -1.01354314 -0.8504215 ]\n",
      " [-0.15419291 -0.48629638 -0.52901952]\n",
      " [-0.00618733 -0.12435261 -0.15226949]]\n",
      "\n",
      "correct scores:\n",
      "[[-0.81233741 -1.27654624 -0.70335995]\n",
      " [-0.17129677 -1.18803311 -0.47310444]\n",
      " [-0.51590475 -1.01354314 -0.8504215 ]\n",
      " [-0.15419291 -0.48629638 -0.52901952]\n",
      " [-0.00618733 -0.12435261 -0.15226949]]\n",
      "\n",
      "Difference between your scores and correct scores:\n",
      "3.6802720496109664e-08\n"
     ]
    }
   ],
   "source": [
    "scores = net.loss(X)\n",
    "print('Your scores:')\n",
    "print(scores)\n",
    "print()\n",
    "print('correct scores:')\n",
    "correct_scores = np.asarray([\n",
    "  [-0.81233741, -1.27654624, -0.70335995],\n",
    "  [-0.17129677, -1.18803311, -0.47310444],\n",
    "  [-0.51590475, -1.01354314, -0.8504215 ],\n",
    "  [-0.15419291, -0.48629638, -0.52901952],\n",
    "  [-0.00618733, -0.12435261, -0.15226949]])\n",
    "print(correct_scores)\n",
    "print()\n",
    "\n",
    "# The difference should be very small. We get < 1e-7\n",
    "print('Difference between your scores and correct scores:')\n",
    "print(np.sum(np.abs(scores - correct_scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forward pass: compute loss\n",
    "In the same function, implement the second part that computes the data and regularization loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference between your loss and correct loss:\n",
      "1.794120407794253e-13\n"
     ]
    }
   ],
   "source": [
    "loss, _ = net.loss(X, y, reg=0.05)\n",
    "correct_loss = 1.30378789133\n",
    "\n",
    "# should be very small, we get < 1e-12\n",
    "print('Difference between your loss and correct loss:')\n",
    "print(np.sum(np.abs(loss - correct_loss)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Backward pass\n",
    "Implement the rest of the function. This will compute the gradient of the loss with respect to the variables `W1`, `b1`, `W2`, and `b2`. Now that you (hopefully!) have a correctly implemented forward pass, you can debug your backward pass using a numeric gradient check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W2 max relative error: 3.440708e-09\n",
      "b2 max relative error: 3.865091e-11\n",
      "W1 max relative error: 3.561318e-09\n",
      "b1 max relative error: 2.738421e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.gradient_check import eval_numerical_gradient\n",
    "\n",
    "# Use numeric gradient checking to check your implementation of the backward pass.\n",
    "# If your implementation is correct, the difference between the numeric and\n",
    "# analytic gradients should be less than 1e-8 for each of W1, W2, b1, and b2.\n",
    "\n",
    "loss, grads = net.loss(X, y, reg=0.05)\n",
    "\n",
    "# these should all be less than 1e-8 or so\n",
    "for param_name in grads:\n",
    "    f = lambda W: net.loss(X, y, reg=0.05)[0]\n",
    "    param_grad_num = eval_numerical_gradient(f, net.params[param_name], verbose=False)\n",
    "    print('%s max relative error: %e' % (param_name, rel_error(param_grad_num, grads[param_name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the network\n",
    "To train the network we will use stochastic gradient descent (SGD), similar to the SVM and Softmax classifiers. Look at the function `TwoLayerNet.train` and fill in the missing sections to implement the training procedure. This should be very similar to the training procedure you used for the SVM and Softmax classifiers. You will also have to implement `TwoLayerNet.predict`, as the training process periodically performs prediction to keep track of accuracy over time while the network trains.\n",
    "\n",
    "Once you have implemented the method, run the code below to train a two-layer network on toy data. You should achieve a training loss less than 0.02."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final training loss:  0.017149607938732037\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "net = init_toy_model()\n",
    "stats = net.train(X, y, X, y,\n",
    "            learning_rate=1e-1, reg=5e-6,\n",
    "            num_iters=100, verbose=False)\n",
    "\n",
    "print('Final training loss: ', stats['loss_history'][-1])\n",
    "\n",
    "# plot the loss history\n",
    "plt.plot(stats['loss_history'])\n",
    "plt.xlabel('iteration')\n",
    "plt.ylabel('training loss')\n",
    "plt.title('Training Loss history')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the data\n",
    "Now that you have implemented a two-layer network that passes gradient checks and works on toy data, it's time to load up our favorite CIFAR-10 data so we can use it to train a classifier on a real dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 3072)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 3072)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "from cs231n.data_utils import load_CIFAR10\n",
    "\n",
    "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):\n",
    "    \"\"\"\n",
    "    Load the CIFAR-10 dataset from disk and perform preprocessing to prepare\n",
    "    it for the two-layer neural net classifier. These are the same steps as\n",
    "    we used for the SVM, but condensed to a single function.  \n",
    "    \"\"\"\n",
    "    # Load the raw CIFAR-10 data\n",
    "    cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "    \n",
    "    # Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "    try:\n",
    "       del X_train, y_train\n",
    "       del X_test, y_test\n",
    "       print('Clear previously loaded data.')\n",
    "    except:\n",
    "       pass\n",
    "\n",
    "    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "        \n",
    "    # Subsample the data\n",
    "    mask = list(range(num_training, num_training + num_validation))\n",
    "    X_val = X_train[mask]\n",
    "    y_val = y_train[mask]\n",
    "    mask = list(range(num_training))\n",
    "    X_train = X_train[mask]\n",
    "    y_train = y_train[mask]\n",
    "    mask = list(range(num_test))\n",
    "    X_test = X_test[mask]\n",
    "    y_test = y_test[mask]\n",
    "\n",
    "    # Normalize the data: subtract the mean image\n",
    "    mean_image = np.mean(X_train, axis=0)\n",
    "    X_train -= mean_image\n",
    "    X_val -= mean_image\n",
    "    X_test -= mean_image\n",
    "\n",
    "    # Reshape data to rows\n",
    "    X_train = X_train.reshape(num_training, -1)\n",
    "    X_val = X_val.reshape(num_validation, -1)\n",
    "    X_test = X_test.reshape(num_test, -1)\n",
    "\n",
    "    return X_train, y_train, X_val, y_val, X_test, y_test\n",
    "\n",
    "\n",
    "# Invoke the above function to get our data.\n",
    "X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a network\n",
    "To train our network we will use SGD. In addition, we will adjust the learning rate with an exponential learning rate schedule as optimization proceeds; after each epoch, we will reduce the learning rate by multiplying it by a decay rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1000: loss 2.302954\n",
      "iteration 100 / 1000: loss 2.302550\n",
      "iteration 200 / 1000: loss 2.297648\n",
      "iteration 300 / 1000: loss 2.259602\n",
      "iteration 400 / 1000: loss 2.204170\n",
      "iteration 500 / 1000: loss 2.118565\n",
      "iteration 600 / 1000: loss 2.051535\n",
      "iteration 700 / 1000: loss 1.988466\n",
      "iteration 800 / 1000: loss 2.006591\n",
      "iteration 900 / 1000: loss 1.951473\n",
      "Validation accuracy:  0.287\n"
     ]
    }
   ],
   "source": [
    "input_size = 32 * 32 * 3\n",
    "hidden_size = 50\n",
    "num_classes = 10\n",
    "net = TwoLayerNet(input_size, hidden_size, num_classes)\n",
    "\n",
    "# Train the network\n",
    "stats = net.train(X_train, y_train, X_val, y_val,\n",
    "            num_iters=1000, batch_size=200,\n",
    "            learning_rate=1e-4, learning_rate_decay=0.95,\n",
    "            reg=0.25, verbose=True)\n",
    "\n",
    "# Predict on the validation set\n",
    "val_acc = (net.predict(X_val) == y_val).mean()\n",
    "print('Validation accuracy: ', val_acc)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Debug the training\n",
    "With the default parameters we provided above, you should get a validation accuracy of about 0.29 on the validation set. This isn't very good.\n",
    "\n",
    "One strategy for getting insight into what's wrong is to plot the loss function and the accuracies on the training and validation sets during optimization.\n",
    "\n",
    "Another strategy is to visualize the weights that were learned in the first layer of the network. In most neural networks trained on visual data, the first layer weights typically show some visible structure when visualized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the loss function and train / validation accuracies\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(stats['loss_history'])\n",
    "plt.title('Loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Loss')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(stats['train_acc_history'], label='train')\n",
    "plt.plot(stats['val_acc_history'], label='val')\n",
    "plt.title('Classification accuracy history')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Classification accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cs231n.vis_utils import visualize_grid\n",
    "\n",
    "# Visualize the weights of the network\n",
    "\n",
    "def show_net_weights(net):\n",
    "    W1 = net.params['W1']\n",
    "    W1 = W1.reshape(32, 32, 3, -1).transpose(3, 0, 1, 2)\n",
    "    plt.imshow(visualize_grid(W1, padding=3).astype('uint8'))\n",
    "    plt.gca().axis('off')\n",
    "    plt.show()\n",
    "\n",
    "show_net_weights(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tune your hyperparameters\n",
    "\n",
    "**What's wrong?**. Looking at the visualizations above, we see that the loss is decreasing more or less linearly, which seems to suggest that the learning rate may be too low. Moreover, there is no gap between the training and validation accuracy, suggesting that the model we used has low capacity, and that we should increase its size. On the other hand, with a very large model we would expect to see more overfitting, which would manifest itself as a very large gap between the training and validation accuracy.\n",
    "\n",
    "**Tuning**. Tuning the hyperparameters and developing intuition for how they affect the final performance is a large part of using Neural Networks, so we want you to get a lot of practice. Below, you should experiment with different values of the various hyperparameters, including hidden layer size, learning rate, numer of training epochs, and regularization strength. You might also consider tuning the learning rate decay, but you should be able to get good performance using the default value.\n",
    "\n",
    "**Approximate results**. You should be aim to achieve a classification accuracy of greater than 48% on the validation set. Our best network gets over 52% on the validation set.\n",
    "\n",
    "**Experiment**: You goal in this exercise is to get as good of a result on CIFAR-10 as you can (52% could serve as a reference), with a fully-connected Neural Network. Feel free implement your own techniques (e.g. PCA to reduce dimensionality, or adding dropout, or adding features to the solver, etc.)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Explain your hyperparameter tuning process below.**\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for hs: 4.000000e+01, lr: 1.000000e-04 and r: 2.000000e-01, valid accuracy is: 0.281000\n",
      "for hs: 4.000000e+01, lr: 1.000000e-04 and r: 4.000000e-01, valid accuracy is: 0.282000\n",
      "for hs: 4.000000e+01, lr: 1.000000e-04 and r: 6.000000e-01, valid accuracy is: 0.287000\n",
      "for hs: 4.000000e+01, lr: 5.000000e-04 and r: 2.000000e-01, valid accuracy is: 0.437000\n",
      "for hs: 4.000000e+01, lr: 5.000000e-04 and r: 4.000000e-01, valid accuracy is: 0.444000\n",
      "for hs: 4.000000e+01, lr: 5.000000e-04 and r: 6.000000e-01, valid accuracy is: 0.448000\n",
      "for hs: 4.000000e+01, lr: 1.000000e-03 and r: 2.000000e-01, valid accuracy is: 0.468000\n",
      "for hs: 4.000000e+01, lr: 1.000000e-03 and r: 4.000000e-01, valid accuracy is: 0.447000\n",
      "for hs: 4.000000e+01, lr: 1.000000e-03 and r: 6.000000e-01, valid accuracy is: 0.483000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cww97/Documents/cs231n/assignment1/cs231n/classifiers/neural_net.py:106: RuntimeWarning: invalid value encountered in true_divide\n",
      "  loss = cors / scores_sums\n",
      "/home/cww97/Documents/cs231n/assignment1/cs231n/classifiers/neural_net.py:107: RuntimeWarning: divide by zero encountered in log\n",
      "  loss = -np.sum(np.log(loss))/N + reg * (np.sum(W1 * W1) + np.sum(W2 * W2))\n",
      "/home/cww97/Documents/cs231n/assignment1/cs231n/classifiers/neural_net.py:120: RuntimeWarning: invalid value encountered in true_divide\n",
      "  s = np.divide(scores, scores_sums.reshape(N, 1))\n",
      "/home/cww97/Documents/cs231n/assignment1/cs231n/classifiers/neural_net.py:121: RuntimeWarning: invalid value encountered in true_divide\n",
      "  s[range(N), y] = - (scores_sums - cors) / scores_sums\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for hs: 4.000000e+01, lr: 5.000000e-03 and r: 2.000000e-01, valid accuracy is: 0.087000\n",
      "for hs: 4.000000e+01, lr: 5.000000e-03 and r: 4.000000e-01, valid accuracy is: 0.087000\n",
      "for hs: 4.000000e+01, lr: 5.000000e-03 and r: 6.000000e-01, valid accuracy is: 0.087000\n",
      "for hs: 6.000000e+01, lr: 1.000000e-04 and r: 2.000000e-01, valid accuracy is: 0.291000\n",
      "for hs: 6.000000e+01, lr: 1.000000e-04 and r: 4.000000e-01, valid accuracy is: 0.276000\n",
      "for hs: 6.000000e+01, lr: 1.000000e-04 and r: 6.000000e-01, valid accuracy is: 0.278000\n",
      "for hs: 6.000000e+01, lr: 5.000000e-04 and r: 2.000000e-01, valid accuracy is: 0.456000\n",
      "for hs: 6.000000e+01, lr: 5.000000e-04 and r: 4.000000e-01, valid accuracy is: 0.446000\n",
      "for hs: 6.000000e+01, lr: 5.000000e-04 and r: 6.000000e-01, valid accuracy is: 0.450000\n",
      "for hs: 6.000000e+01, lr: 1.000000e-03 and r: 2.000000e-01, valid accuracy is: 0.449000\n",
      "for hs: 6.000000e+01, lr: 1.000000e-03 and r: 4.000000e-01, valid accuracy is: 0.474000\n",
      "for hs: 6.000000e+01, lr: 1.000000e-03 and r: 6.000000e-01, valid accuracy is: 0.449000\n",
      "for hs: 6.000000e+01, lr: 5.000000e-03 and r: 2.000000e-01, valid accuracy is: 0.087000\n",
      "for hs: 6.000000e+01, lr: 5.000000e-03 and r: 4.000000e-01, valid accuracy is: 0.087000\n",
      "for hs: 6.000000e+01, lr: 5.000000e-03 and r: 6.000000e-01, valid accuracy is: 0.087000\n",
      "for hs: 8.000000e+01, lr: 1.000000e-04 and r: 2.000000e-01, valid accuracy is: 0.287000\n",
      "for hs: 8.000000e+01, lr: 1.000000e-04 and r: 4.000000e-01, valid accuracy is: 0.288000\n",
      "for hs: 8.000000e+01, lr: 1.000000e-04 and r: 6.000000e-01, valid accuracy is: 0.287000\n",
      "for hs: 8.000000e+01, lr: 5.000000e-04 and r: 2.000000e-01, valid accuracy is: 0.468000\n",
      "for hs: 8.000000e+01, lr: 5.000000e-04 and r: 4.000000e-01, valid accuracy is: 0.449000\n",
      "for hs: 8.000000e+01, lr: 5.000000e-04 and r: 6.000000e-01, valid accuracy is: 0.448000\n",
      "for hs: 8.000000e+01, lr: 1.000000e-03 and r: 2.000000e-01, valid accuracy is: 0.477000\n",
      "for hs: 8.000000e+01, lr: 1.000000e-03 and r: 4.000000e-01, valid accuracy is: 0.453000\n",
      "for hs: 8.000000e+01, lr: 1.000000e-03 and r: 6.000000e-01, valid accuracy is: 0.482000\n",
      "for hs: 8.000000e+01, lr: 5.000000e-03 and r: 2.000000e-01, valid accuracy is: 0.087000\n",
      "for hs: 8.000000e+01, lr: 5.000000e-03 and r: 4.000000e-01, valid accuracy is: 0.087000\n",
      "for hs: 8.000000e+01, lr: 5.000000e-03 and r: 6.000000e-01, valid accuracy is: 0.087000\n",
      "for hs: 1.000000e+02, lr: 1.000000e-04 and r: 2.000000e-01, valid accuracy is: 0.289000\n",
      "for hs: 1.000000e+02, lr: 1.000000e-04 and r: 4.000000e-01, valid accuracy is: 0.302000\n",
      "for hs: 1.000000e+02, lr: 1.000000e-04 and r: 6.000000e-01, valid accuracy is: 0.284000\n",
      "for hs: 1.000000e+02, lr: 5.000000e-04 and r: 2.000000e-01, valid accuracy is: 0.455000\n",
      "for hs: 1.000000e+02, lr: 5.000000e-04 and r: 4.000000e-01, valid accuracy is: 0.457000\n",
      "for hs: 1.000000e+02, lr: 5.000000e-04 and r: 6.000000e-01, valid accuracy is: 0.455000\n",
      "for hs: 1.000000e+02, lr: 1.000000e-03 and r: 2.000000e-01, valid accuracy is: 0.474000\n",
      "for hs: 1.000000e+02, lr: 1.000000e-03 and r: 4.000000e-01, valid accuracy is: 0.466000\n",
      "for hs: 1.000000e+02, lr: 1.000000e-03 and r: 6.000000e-01, valid accuracy is: 0.460000\n",
      "for hs: 1.000000e+02, lr: 5.000000e-03 and r: 2.000000e-01, valid accuracy is: 0.087000\n",
      "for hs: 1.000000e+02, lr: 5.000000e-03 and r: 4.000000e-01, valid accuracy is: 0.087000\n",
      "for hs: 1.000000e+02, lr: 5.000000e-03 and r: 6.000000e-01, valid accuracy is: 0.087000\n",
      "for hs: 1.200000e+02, lr: 1.000000e-04 and r: 2.000000e-01, valid accuracy is: 0.293000\n",
      "for hs: 1.200000e+02, lr: 1.000000e-04 and r: 4.000000e-01, valid accuracy is: 0.296000\n",
      "for hs: 1.200000e+02, lr: 1.000000e-04 and r: 6.000000e-01, valid accuracy is: 0.287000\n",
      "for hs: 1.200000e+02, lr: 5.000000e-04 and r: 2.000000e-01, valid accuracy is: 0.466000\n",
      "for hs: 1.200000e+02, lr: 5.000000e-04 and r: 4.000000e-01, valid accuracy is: 0.460000\n",
      "for hs: 1.200000e+02, lr: 5.000000e-04 and r: 6.000000e-01, valid accuracy is: 0.444000\n",
      "for hs: 1.200000e+02, lr: 1.000000e-03 and r: 2.000000e-01, valid accuracy is: 0.487000\n",
      "for hs: 1.200000e+02, lr: 1.000000e-03 and r: 4.000000e-01, valid accuracy is: 0.458000\n",
      "for hs: 1.200000e+02, lr: 1.000000e-03 and r: 6.000000e-01, valid accuracy is: 0.472000\n",
      "for hs: 1.200000e+02, lr: 5.000000e-03 and r: 2.000000e-01, valid accuracy is: 0.087000\n",
      "for hs: 1.200000e+02, lr: 5.000000e-03 and r: 4.000000e-01, valid accuracy is: 0.087000\n",
      "for hs: 1.200000e+02, lr: 5.000000e-03 and r: 6.000000e-01, valid accuracy is: 0.087000\n",
      "Best Networks has an Accuracy of: 0.487000\n"
     ]
    }
   ],
   "source": [
    "best_net = None # store the best model into this \n",
    "\n",
    "#################################################################################\n",
    "# TODO: Tune hyperparameters using the validation set. Store your best trained  #\n",
    "# model in best_net.                                                            #\n",
    "#                                                                               #\n",
    "# To help debug your network, it may help to use visualizations similar to the  #\n",
    "# ones we used above; these visualizations will have significant qualitative    #\n",
    "# differences from the ones we saw above for the poorly tuned network.          #\n",
    "#                                                                               #\n",
    "# Tweaking hyperparameters by hand can be fun, but you might find it useful to  #\n",
    "# write code to sweep through possible combinations of hyperparameters          #\n",
    "# automatically like we did on the previous exercises.                          #\n",
    "#################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "hidden_size = [40, 60, 80, 100, 120]\n",
    "learning_rate = [1e-4, 5e-4, 1e-3, 5e-3]\n",
    "reg = [0.2, 0.4, 0.6]\n",
    "\n",
    "best_acc = -1\n",
    "log = {}\n",
    "\n",
    "for hs in hidden_size:\n",
    "    for lr in learning_rate:\n",
    "        for r in reg:\n",
    "            \n",
    "            # Set up the network\n",
    "            net = TwoLayerNet(input_size, hs, num_classes)\n",
    "\n",
    "            # Train the network\n",
    "            stats = net.train(X_train, y_train, X_val, y_val,\n",
    "                        num_iters=1000, batch_size=200,\n",
    "                        learning_rate=lr, learning_rate_decay=0.95,\n",
    "                        reg=r, verbose=False)\n",
    "            \n",
    "            acc = stats['val_acc_history'][-1]\n",
    "            log[(hs, lr, r)] = acc\n",
    "            \n",
    "            # Print Log\n",
    "            print('for hs: %e, lr: %e and r: %e, valid accuracy is: %f' \n",
    "                    % (hs, lr, r, acc))\n",
    "            \n",
    "            if acc > best_acc:\n",
    "                best_net = net\n",
    "                best_acc = acc\n",
    "                \n",
    "print('Best Networks has an Accuracy of: %f' % best_acc)\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the weights of the best network\n",
    "show_net_weights(best_net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run on the test set\n",
    "When you are done experimenting, you should evaluate your final trained network on the test set; you should get above 48%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy:  0.489\n"
     ]
    }
   ],
   "source": [
    "test_acc = (best_net.predict(X_test) == y_test).mean()\n",
    "print('Test accuracy: ', test_acc)"
   ]
  },
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    "**Inline Question**\n",
    "\n",
    "Now that you have trained a Neural Network classifier, you may find that your testing accuracy is much lower than the training accuracy. In what ways can we decrease this gap? Select all that apply.\n",
    "\n",
    "1. Train on a larger dataset.\n",
    "2. Add more hidden units.\n",
    "3. Increase the regularization strength.\n",
    "4. None of the above.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ 1,3\n",
    "\n",
    "$\\color{blue}{\\textit Your Explanation:}$ 2 may cause overfit, 另两个都可以增强模型泛化性\n",
    "\n"
   ]
  }
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